Robust image hashing using exact Gaussian–Hermite moments

Yuan

2020

IEEE Access

In order to improve classification performance of the hash algorithm for color images, this paper presents an hash algorithm that based on three-dimensional color structure features and luminance gradient feature. Firstly, the algorithm preprocesses the input image to produce a secondary image, and extracts the color opponent component from the secondary image. Secondly, three-dimensional external structural features are constructed by using the peak curve, mean curve, and valley curve of the color opponent component. Thirdly, the overlapping points of the peak points in different visual angles and the valley points in different visual angles are selected as the salient points. The position information of the salient points are used to construct internal features. The gradient feature is constructed by using pixel changes in four directions of Y component of luminance image in YCbCr color space. Finally, the three-dimensional color structure features and luminance gradient feature are combined and disturbed to obtain the final hash sequence. Experiment results show that the proposed scheme has preferable robustness and discrimination. Compared with some existing schemes, the proposed scheme has better classification performance, shorter hash length and less average time of hash generation, and it has good detection performance in image copy detection and image tampered detection. INDEX TERMS Image hashing, copy and tampered detection, luminance gradient, three-dimensional color features.

Section: Other Image Hashing Methodsmentioning

confidence: 99%

Section: Other Image Hashing Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Perceptual Image Hashing Based on Color Structure and Intensity Gradient

Yuan

2020

IEEE Access

“…Hosny et al [14] proposed a hashing scheme based on quaternion polar complex exponential transform, which has good robustness and image authentication ability. Hosny et al [15] proposed a hashing scheme based on Gaussian-Hermite moments. And this algorithm has robustness not only to some common noises, but also to rotation attacks.…”

Section: Related Workmentioning

confidence: 99%

Robust Image Hashing Based on Cool and Warm Hue and Space Angle

Security and Communication Networks

Liu

2021

Image hashing has attracted more and more attention in the field of information security. In this paper, a novel hashing algorithm using cool and warm hue information and three-dimensional space angle is proposed. Firstly, the original image is preprocessed to get the opposite color component and the hue component H in HSV color space. Then, the distribution of cool and warm hue pixels is extracted from hue component H. Blocks the hue component H, according to the proportion of warm hue and cool hue pixels in each small block, combined with the quaternion and opposite color component, constructed the cool and warm hue opposite color quaternion (CWOCQ) feature. Then, three-dimensional space, opposite color, and cool and warm hue are combined to obtain the three-dimensional space angle (TDSA) feature. The CWOCQ feature and the TDSA feature are connected and disturbed to obtain the final hash sequence. Experimental results show that the proposed algorithm has good security and has better image classification performance and shorter computation time compared with some advanced algorithms.

“…Huang et al [26] extracted the statistical features of the texture image such as contrast, correlation, gradient, and homogeneity as the global features of the image, and combined them with the DCT transform to construct the image hash. Hosny et al [27] extracted Gaussian-Hermite moments and variance of grayscale images as image features. Wang et al [28] designed hash sequences by combining the Watson visual model, Zernike moments and DCT coefficients.…”

Section: Based On Statistical Characteristicsmentioning

confidence: 99%

Perceptual Image Hashing Based on Three-Dimensional Global Features and Image Energy

Yuan

2021

IEEE Access

In order to improve classification performance and operating efficiency of the hash algorithm, this paper proposes a novel hash algorithm that combines three-dimensional global features and local energy features. During the stage of three-dimensional features extraction, the image is firstly compressed by SVD decomposition to form a secondary image. Then the statistical features of the secondary image at the threedimensional visual angle are extracted as the global features. Finally, the global feature hash is generated by using the relationship between the statistical features of the image layers from different three-dimensional visual angles. In the energy feature extraction stage, the luminance image is divided into blocks, and then the energy value of each image sub-block is obtained. The multi-directional energy change features are taken as the local features of the image. Subsequent experimental results prove the effectiveness of the proposed algorithm. The algorithm not only has good robustness to the conventional content-preserving operations, but also achieves a good balance between discrimination capability and robustness. In addition, compared with several state-of-the-art schemes, this algorithm has the best ROC curve, the shortest running time and the best local tamper detection ability.